"""
Tests the Coulomb friction interaction between an agent and a wall as the agent slides.
Tests cover:
- Time and position continuity
- Near-zero angular velocity (omega), constant orientation (theta)
- Velocity along the x-axis should be positive during the core simulation
- Velocity along the y-axis should be either near zero or negative during the core simulation
"""
# Copyright 2025 Institute of Light and Matter, CNRS UMR 5306, University Claude Bernard Lyon 1
# Contributors: Oscar DUFOUR, Maxime STAPELLE, Alexandre NICOLAS
# This software is a computer program designed to generate a realistic crowd from anthropometric data and
# simulate the mechanical interactions that occur within it and with obstacles.
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import subprocess
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from configuration.backup import xml_to_Chaos
#: Tolerance for the constancy of the decisional time step used throughout the simulation (s).
TIME_TOL = 1e-4
#: Maximum allowed spatial jump (m) between consecutive time steps for the agent.
MAX_SPATIAL_JUMP = 1
#: Opposite of the minimum allowed velocity along x during core simulation (m/s).
VX_TOL = 1e-2
#: Maximum allowed velocity along y during core simulation (m/s).
VY_TOL = 1e-2
#: Tolerance for near-zero angular velocities of all agents during the whole simulation (rad/s).
OMEGA_CONTACT_TOL = 0.5
#: Maximum allowed range for orientation (theta) of all agents during the whole simulation (radians).
DELTA_THETA_CONTACT_TOL = 0.5 # radians
[docs]
@pytest.fixture(scope="session")
def df() -> pd.DataFrame:
"""
Export to CSV the XML files and load the time series once per test session.
Returns
-------
pd.DataFrame
DataFrame containing all time series.
"""
subprocess.run(
["uv", "run", "python", "run_simulation.py"],
check=True,
)
filenameCSV = "all_trajectories.csv" # Name of the final CSV file we’ll generate
PathXML = Path("inputXML") # Folder path where the XML files are located
PathCSV = Path("inputCSV") # Folder path where CSV files will be saved
PathCSV.mkdir(parents=True, exist_ok=True) # Create directories if it doesn't exist
xml_to_Chaos.export_XML_to_CSV(PathCSV, PathXML)
return pd.read_csv(PathCSV / filenameCSV)
[docs]
def test_time_and_position_continuity(df: pd.DataFrame) -> None:
"""
Test time and position continuity for each agent.
Parameters
----------
df : pd.DataFrame
DataFrame containing all time series.
"""
required_cols = {"ID", "t", "x", "y"}
missing = required_cols - set(df.columns)
assert not missing, f"Missing expected columns: {missing}"
# agent IDs with irregular time steps
violations_missing_time: list[int] = []
# (agent_id, list of jump distances > MAX_SPATIAL_JUMP)
violations_big_jump: list[tuple[int, list[float]]] = []
for agent_id, g in df.sort_values("t").groupby("ID"):
t = g["t"].to_numpy()
dt = np.diff(t)
ddt = np.diff(dt)
if not np.all(np.abs(ddt) < TIME_TOL):
violations_missing_time.append(int(agent_id))
x = g["x"].to_numpy()
y = g["y"].to_numpy()
dist = np.sqrt(np.diff(x) ** 2 + np.diff(y) ** 2)
bad_jump_idx = np.where(dist > MAX_SPATIAL_JUMP)[0]
if bad_jump_idx.size > 0:
violations_big_jump.append((int(agent_id), dist[bad_jump_idx].tolist()))
assert not violations_missing_time, f"Irregular time steps: {violations_missing_time}"
assert not violations_big_jump, f"Large spatial jumps: {violations_big_jump}"
[docs]
def test_omega_near_zero_and_theta_near_constant(df: pd.DataFrame) -> None:
"""
Near-zero angular velocity and near constant orientation for all agents.
Parameters
----------
df : pd.DataFrame
DataFrame containing all time series.
"""
required_cols = {"ID", "omega", "theta"}
missing = required_cols - set(df.columns)
assert not missing, f"Missing expected columns: {missing}"
violations_omega: list[tuple[int, float]] = []
violations_theta: list[tuple[int, float]] = []
for agent_id, g in df.groupby("ID"):
max_abs_omega = float(g["omega"].abs().max())
if max_abs_omega > OMEGA_CONTACT_TOL:
violations_omega.append((agent_id, max_abs_omega))
theta = g["theta"].to_numpy()
theta_range = float(theta.max() - theta.min())
if theta_range > DELTA_THETA_CONTACT_TOL:
violations_theta.append((agent_id, theta_range))
assert not violations_omega, f"omega not ~0 for some agents: {violations_omega}"
assert not violations_theta, f"theta not constant for some agents: {violations_theta}"
[docs]
def test_velocity_signs_during_core(df: pd.DataFrame) -> None:
"""
During the core of the simulation: vx > 0 and vy ~ 0 or negative where "core" is defined as the central 80% of the simulation time.
Parameters
----------
df : pd.DataFrame
DataFrame containing all time series.
"""
required_cols = {"ID", "t", "vx", "vy"}
missing = required_cols - set(df.columns)
assert not missing, f"Missing expected columns: {missing}"
g = df[df["ID"] == 0].sort_values("t")
assert not g.empty, "No data for slip agent (ID 0)"
t = g["t"].to_numpy()
t_min, t_max = float(t.min()), float(t.max())
# Define "core" as the central 80% of the simulation time
core = g[(g["t"] >= t_min + 0.1 * (t_max - t_min)) & (g["t"] <= t_min + 0.9 * (t_max - t_min))]
assert not core.empty, "Core simulation window is empty"
bad_vx = core[core["vx"] <= -VX_TOL]
if not bad_vx.empty:
raise AssertionError(f"Non-positive vx during core:\n{bad_vx[['t', 'vx']]}")
bad_vy = core[core["vy"] > VY_TOL]
if not bad_vy.empty:
raise AssertionError(f"Positive vy above tolerance during core:\n{bad_vy[['t', 'vy']]}")